Computational cognitive models are powerful tools that explain how representations inperception and memory are transformed into observed behavior—such as choice and responsetime—at the level of individual participants and individual trials. Such models have been usedacross domains in cognitive science to build integrative theories of phenomena likecategorization, recognition, and attentional learning. Many of these models, however, weredeveloped to account for data from visual tasks, so it remains unclear whether the samemechanisms apply in the auditory domain. Thus, theories of, for example, auditory memory havenot benefited from modeling techniques to the same extent as have theories of visual memory. Toaddress this gap, we illustrate how a cognitive model that was originally developed in the visualdomain, the Exemplar-Based Random Walk model (EBRW; Nosofsky Palmeri, 1997), cannonetheless account for choice and response time in recognition of novel auditory timbres. Wedo so by using the EBRW to couple stimulus representations derived from multidimensionalscaling to a process by which recognition decisions are made by accumulating evidence thatdepends on similarity. This paper therefore offers a practical tutorial on addressing the uniquechallenges of adapting computational cognitive models to psychoacoustic data, highlighting theutility of applying such models in revealing connections between seemingly disparate aspects ofauditory cognition.
Gillespie et al. (Sat,) studied this question.